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Matchado MS, Rühlemann M, Reitmeier S, Kacprowski T, Frost F, Haller D, Baumbach J, List M. On the limits of 16S rRNA gene-based metagenome prediction and functional profiling. Microb Genom 2024; 10:001203. [PMID: 38421266 PMCID: PMC10926695 DOI: 10.1099/mgen.0.001203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 02/05/2024] [Indexed: 03/02/2024] Open
Abstract
Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.
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Affiliation(s)
- Monica Steffi Matchado
- Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
| | - Malte Rühlemann
- Institute of Clinical Molecular Biology, Kiel University, Kiel, Germany
| | - Sandra Reitmeier
- ZIEL - Institute for Food & Health, Core Facility Microbiome, Technical University of Munich, Freising, Germany
| | - Tim Kacprowski
- Division Data Science in Biomedicine, Peter L. Reichertz Institute for Medical Informatics of Technische Universität Braunschweig and Hannover Medical School, Braunschweig, Germany
- Braunschweig Integrated Centre of Systems Biology (BRICS), TU Braunschweig, Braunschweig, Germany
- Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research (HZI), Braunschweig, Germany
| | - Fabian Frost
- Department of Medicine A, University Medicine Greifswald, Greifswald, Germany
| | - Dirk Haller
- ZIEL - Institute for Food & Health, Core Facility Microbiome, Technical University of Munich, Freising, Germany
- Chair of Nutrition and Immunology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
| | - Jan Baumbach
- Institute for Computational Systems Biology, University of Hamburg, Hamburg, Germany
- Institute of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Markus List
- Data Science in Systems Biology, TUM School of Life Sciences, Technical University of Munich, Freising, Germany
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Cerk K, Ugalde‐Salas P, Nedjad CG, Lecomte M, Muller C, Sherman DJ, Hildebrand F, Labarthe S, Frioux C. Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing. Microb Biotechnol 2024; 17:e14396. [PMID: 38243750 PMCID: PMC10832553 DOI: 10.1111/1751-7915.14396] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 11/27/2023] [Accepted: 12/20/2023] [Indexed: 01/21/2024] Open
Abstract
Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.
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Affiliation(s)
- Klara Cerk
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | | | - Chabname Ghassemi Nedjad
- Inria, University of Bordeaux, INRAETalenceFrance
- University of Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800TalenceFrance
| | - Maxime Lecomte
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE STLO¸University of RennesRennesFrance
| | | | | | - Falk Hildebrand
- Quadram Institute BioscienceNorwichUK
- Earlham InstituteNorwichUK
| | - Simon Labarthe
- Inria, University of Bordeaux, INRAETalenceFrance
- INRAE, University of Bordeaux, BIOGECO, UMR 1202CestasFrance
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Huang S, Kong Y, Chen Y, Huang X, Ma P, Liu X. Microbial denitrification characteristics of typical decentralized wastewater treatment processes based on 16S rRNA sequencing. Front Microbiol 2023; 14:1242506. [PMID: 37779708 PMCID: PMC10537219 DOI: 10.3389/fmicb.2023.1242506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Accepted: 09/04/2023] [Indexed: 10/03/2023] Open
Abstract
Despite the widespread application of decentralized wastewater treatment (WWT) facilities in China, relatively few research has used the multi-media biological filter (MMBF) facilities to investigate the microorganism characteristics. This study utilizes 16S rRNA high-throughput sequencing (HTS) technology to examine the microbial biodiversity of a representative wastewater treatment (WWT) system in an expressway service area. The pathways of nitrogen removal along the treatment route were analyzed in conjunction with water quality monitoring. The distribution and composition of microbial flora in the samples were examined, and the dominant flora were identified using LEfSe analysis. The FAPROTAX methodology was employed to investigate the relative abundance of genes associated with the nitrogen cycle and to discern the presence of functional genes involved in nitrogen metabolism. On average, the method has a high level of efficiency in removing COD, TN, NH3-N, and TP from the effluent. The analysis of the microbial community identified a total of 40 phyla, 111 classes, 143 orders, 263 families, and 419 genera. The phyla that were predominantly observed include Proteobacteria, Acidobacteria, Chloroflexi, Actinobacteria, Nitrospirae, Bacteroidetes. The results show that the system has achieved high performance in nitrogen removal, the abundance of nitrification genes is significantly higher than that of other nitrogen cycle genes such as denitrification, and there are six nitrogen metabolism pathways, primarily nitrification, among which Nitrospirae and Nitrospira are the core differentiated flora that can adapt to low temperature conditions and participate in nitrification, and are the dominant nitrogen removal flora in cold regions. This work aims to comprehensively investigate the diversity and functional properties of the bacterial community in decentralized WWT processes.
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Affiliation(s)
- Shanqian Huang
- Center of Environment Protection, China Academy of Transportation Sciences, Beijing, China
| | - Yaping Kong
- Center of Environment Protection, China Academy of Transportation Sciences, Beijing, China
| | - Yao Chen
- Center of Environment Protection, China Academy of Transportation Sciences, Beijing, China
| | - Xuewen Huang
- Anhui Transportation Holding Group CO., LTD., Hefei, China
| | - Pengfei Ma
- Qinghai Expressway Maintenance Service CO., LTD., Xining, China
| | - Xuexin Liu
- Center of Environment Protection, China Academy of Transportation Sciences, Beijing, China
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4
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Zhang W, Fan X, Shi H, Li J, Zhang M, Zhao J, Su X. Comprehensive Assessment of 16S rRNA Gene Amplicon Sequencing for Microbiome Profiling across Multiple Habitats. Microbiol Spectr 2023; 11:e0056323. [PMID: 37102867 PMCID: PMC10269731 DOI: 10.1128/spectrum.00563-23] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 04/10/2023] [Indexed: 04/28/2023] Open
Abstract
The 16S rRNA gene works as a rapid and effective marker for the identification of microorganisms in complex communities; hence, a huge number of microbiomes have been surveyed by 16S amplicon-based sequencing. The resolution of the 16S rRNA gene is always considered only at the genus level; however, it has not been verified on a wide range of microbes yet. To fully explore the ability and potential of the 16S rRNA gene in microbial profiling, here, we propose Qscore, a comprehensive method to evaluate the performance of amplicons by integrating the amplification rate, multitier taxonomic annotation, sequence type, and length. Our in silico assessment by a "global view" of 35,889 microbe species across multiple reference databases summarizes the optimal sequencing strategy for 16S short reads. On the other hand, since microbes are unevenly distributed according to their habitats, we also provide the recommended configuration for 16 typical ecosystems based on the Qscores of 157,390 microbiomes in the Microbiome Search Engine (MSE). Detailed data simulation further proves that the 16S amplicons produced with Qscore-suggested parameters exhibit high precision in microbiome profiling, which is close to that of shotgun metagenomes under CAMI metrics. Therefore, by reconsidering the precision of 16S-based microbiome profiling, our work not only enables the high-quality reusability of massive sequence legacy that has already been produced but is also significant for guiding microbiome studies in the future. We have implemented the Qscore as an online service at http://qscore.single-cell.cn to parse the recommended sequencing strategy for specific habitats or expected microbial structures. IMPORTANCE 16S rRNA has long been used as a biomarker to identify distinct microbes from complex communities. However, due to the influence of the amplification region, sequencing type, sequence processing, and reference database, the accuracy of 16S rRNA has not been fully verified on a global range. More importantly, the microbial composition of different habitats varies greatly, and it is necessary to adopt different strategies according to the corresponding target microbes to achieve optimal analytical performance. Here, we developed Qscore, which evaluates the comprehensive performance of 16S amplicons from multiple perspectives, thus providing the best sequencing strategies for common ecological environments by using big data.
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Affiliation(s)
- Wenke Zhang
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Xiaoqian Fan
- Shouguang Hospital of Traditional Chinese Medicine, Weifang, China
| | - Haobo Shi
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Jian Li
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Mingqian Zhang
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Jin Zhao
- College of Computer Science and Technology, Qingdao University, Qingdao, China
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao, China
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5
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The association between the respiratory tract microbiome and clinical outcomes in patients with COPD. Microbiol Res 2023; 266:127244. [DOI: 10.1016/j.micres.2022.127244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/25/2022] [Accepted: 10/25/2022] [Indexed: 11/06/2022]
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Establishment and Validation of a New Analysis Strategy for the Study of Plant Endophytic Microorganisms. Int J Mol Sci 2022; 23:ijms232214223. [PMID: 36430699 PMCID: PMC9697482 DOI: 10.3390/ijms232214223] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 11/11/2022] [Accepted: 11/14/2022] [Indexed: 11/19/2022] Open
Abstract
Amplicon sequencing of bacterial or fungal marker sequences is currently the main method for the study of endophytic microorganisms in plants. However, it cannot obtain all types of microorganisms, including bacteria, fungi, protozoa, etc., in samples, nor compare the relative content between endophytic microorganisms and plants and between different types of endophytes. Therefore, it is necessary to develop a better analysis strategy for endophytic microorganism investigation. In this study, a new analysis strategy was developed to obtain endophytic microbiome information from plant transcriptome data. Results showed that the new strategy can obtain the composition of microbial communities and the relative content between plants and endophytic microorganisms, and between different types of endophytic microorganisms from the plant transcriptome data. Compared with the amplicon sequencing method, more endophytic microorganisms and relative content information can be obtained with the new strategy, which can greatly broaden the research scope and save the experimental cost. Furthermore, the advantages and effectiveness of the new strategy were verified with different analysis of the microbial composition, correlation analysis, inoculant content test, and repeatability test.
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7
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Trego A, Keating C, Nzeteu C, Graham A, O’Flaherty V, Ijaz UZ. Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data. Microorganisms 2022; 10:1961. [PMID: 36296237 PMCID: PMC9609705 DOI: 10.3390/microorganisms10101961] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 09/29/2022] [Accepted: 09/30/2022] [Indexed: 11/07/2022] Open
Abstract
Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.
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Affiliation(s)
- Anna Trego
- Microbial Ecology Laboratory, School of Biological and Chemical Sciences and the Ryan Institute, University of Galway, University Road, H91 TK33 Galway, Ireland
| | - Ciara Keating
- Institute of Biodiversity, Animal Health & Comparative Medicine, The University of Glasgow, Oakfield Avenue, Glasgow G12 8LT, UK
| | - Corine Nzeteu
- Microbial Ecology Laboratory, School of Biological and Chemical Sciences and the Ryan Institute, University of Galway, University Road, H91 TK33 Galway, Ireland
| | - Alison Graham
- Microbial Ecology Laboratory, School of Biological and Chemical Sciences and the Ryan Institute, University of Galway, University Road, H91 TK33 Galway, Ireland
| | - Vincent O’Flaherty
- Microbial Ecology Laboratory, School of Biological and Chemical Sciences and the Ryan Institute, University of Galway, University Road, H91 TK33 Galway, Ireland
| | - Umer Zeeshan Ijaz
- Water Engineering Group, School of Engineering, The University of Glasgow, Oakfield Avenue, Glasgow G12 8LT, UK
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8
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Wang J, Qie J, Zhu D, Zhang X, Zhang Q, Xu Y, Wang Y, Mi K, Pei Y, Liu Y, Ji G, Liu X. The landscape in the gut microbiome of long-lived families reveals new insights on longevity and aging - relevant neural and immune function. Gut Microbes 2022; 14:2107288. [PMID: 35939616 PMCID: PMC9361766 DOI: 10.1080/19490976.2022.2107288] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/04/2023] Open
Abstract
Human longevity has a strong familial and genetic component. Dynamic characteristics of the gut microbiome during aging associated with longevity, neural, and immune function remained unknown. Here, we aim to reveal the synergistic changes in gut microbiome associated with decline in neural and immune system with aging and further obtain insights into the establishment of microbiome homeostasis that can benefit human longevity. Based on 16S rRNA and metagenomics sequencing data for 32 longevity families including three generations, centenarians, elderly, and young groups, we found centenarians showed increased diversity of gut microbiota, severely damaged connection among bacteria, depleted in microbial-associated essential amino acid function, and increased abundance of anti-inflammatory bacteria in comparison to young and elderly groups. Some potential probiotic species, such as Desulfovibrio piger, Gordonibacter pamelaeae, Odoribacter splanchnicus, and Ruminococcaceae bacterium D5 were enriched with aging, which might possibly support health maintenance. The level of Amyloid-β (Aβ) and brain-derived neurotrophic factor (BDNF) related to neural function showed increased and decreased with aging, respectively. The elevated level of inflammatory factors was observed in centenarians compared with young and elderly groups. The enriched Bacteroides fragilis in centenarians might promote longevity through up-regulating anti-inflammatory factor IL-10 expression to mediate the critical balance between health and disease. Impressively, the associated analysis for gut microbiota with the level of Aβ, BDNF, and inflammatory factors suggests Bifidobacterium pseudocatenulatum could be a particularly beneficial bacteria in the improvement of impaired neural and immune function. Our results provide a rationale for targeting the gut microbiome in future clinical applications of aging-related diseases and extending life span.Abbreviations: 16S rRNA: 16S ribosomal RNA; MAGs: Metagenome-assembled genomes; ASVs: Amplicon sequence variants; DNA: Deoxyribonucleic acid; FDR: False discovery rate: KEGG: Kyoto Encyclopedia of Genes and Genomes; PCoA: Principal coordinates analysis; PCR: Polymerase chain reaction; PICRUSt: Phylogenetic Investigation of Communities by Reconstruction of Unobserved States; Aβ: Amyloid-β (Aβ); BDNF: Brain-derived neurotrophic factor.
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Affiliation(s)
- Jingjing Wang
- Department of Gastroenterology, Key Laboratory of Holistic Integrative Enterology, The Second Affiliated Hospital of Nanjing Medical University, Jiangsu, China,Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China,The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China,Jiangsu KeyLaboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Jiangsu, China
| | - Jinlong Qie
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Danrong Zhu
- Department of Gastroenterology, Key Laboratory of Holistic Integrative Enterology, The Second Affiliated Hospital of Nanjing Medical University, Jiangsu, China
| | - Xuemei Zhang
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Qingqing Zhang
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Yuyu Xu
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Yipeng Wang
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Kai Mi
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Yang Pei
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Yang Liu
- Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China
| | - Guozhong Ji
- Department of Gastroenterology, Key Laboratory of Holistic Integrative Enterology, The Second Affiliated Hospital of Nanjing Medical University, Jiangsu, China,Jiangsu KeyLaboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Jiangsu, China,Guozhong Ji
| | - Xingyin Liu
- Department of Gastroenterology, Key Laboratory of Holistic Integrative Enterology, The Second Affiliated Hospital of Nanjing Medical University, Jiangsu, China,Department of Pathogen Biology-Microbiology division, State Key Laboratory of Reproductive Medicine, Key Laboratory of Pathogen of Jiangsu Province, Key Laboratory of Human Functional Genomics of Jiangsu Province, Center of Global Health, Nanjing Medical University, Jiangsu, China,The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Suzhou, China,Jiangsu KeyLaboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Jiangsu, China,CONTACT Xingyin Liu Department of Gastroenterology, Key Laboratory of Holistic Integrative Enterology, The Second Affiliated Hospital of Nanjing Medical University, Nanjing210011, China
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Chen Y, Li J, Zhang Y, Zhang M, Sun Z, Jing G, Huang S, Su X. Parallel-Meta Suite: Interactive and rapid microbiome data analysis on multiple platforms. IMETA 2022; 1:e1. [PMID: 38867729 PMCID: PMC10989749 DOI: 10.1002/imt2.1] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Revised: 12/13/2021] [Accepted: 12/17/2021] [Indexed: 06/14/2024]
Abstract
Massive microbiome sequencing data has been generated, which elucidates associations between microbes and their environmental phenotypes such as host health or ecosystem status. Outstanding bioinformatic tools are the basis to decipher the biological information hidden under microbiome data. However, most approaches placed difficulties on the accessibility to nonprofessional users. On the other side, the computing throughput has become a significant bottleneck of many analytical pipelines in processing large-scale datasets. In this study, we introduce Parallel-Meta Suite (PMS), an interactive software package for fast and comprehensive microbiome data analysis, visualization, and interpretation. It covers a wide array of functions for data preprocessing, statistics, visualization by state-of-the-art algorithms in a user-friendly graphical interface, which is accessible to diverse users. To meet the rapidly increasing computational demands, the entire procedure of PMS has been optimized by a parallel computing scheme, enabling the rapid processing of thousands of samples. PMS is compatible with multiple platforms, and an installer has been integrated for full-automatic installation.
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Affiliation(s)
- Yuzhu Chen
- College of Computer Science and TechnologyQingdao UniversityQingdaoShandongChina
| | - Jian Li
- College of Computer Science and TechnologyQingdao UniversityQingdaoShandongChina
| | - Yufeng Zhang
- College of Computer Science and TechnologyQingdao UniversityQingdaoShandongChina
| | - Mingqian Zhang
- College of Computer Science and TechnologyQingdao UniversityQingdaoShandongChina
| | - Zheng Sun
- Single‐Cell Center, Qingdao Institute of BioEnergy and Bioprocess TechnologyChinese Academy of SciencesQingdaoShandongChina
| | - Gongchao Jing
- Single‐Cell Center, Qingdao Institute of BioEnergy and Bioprocess TechnologyChinese Academy of SciencesQingdaoShandongChina
| | - Shi Huang
- Faculty of DentistryThe University of Hong KongHong KongHong Kong SARChina
| | - Xiaoquan Su
- College of Computer Science and TechnologyQingdao UniversityQingdaoShandongChina
- Single‐Cell Center, Qingdao Institute of BioEnergy and Bioprocess TechnologyChinese Academy of SciencesQingdaoShandongChina
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10
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Sun Z, Liu X, Jing G, Chen Y, Jiang S, Zhang M, Liu J, Xu J, Su X. Comprehensive understanding to the public health risk of environmental microbes via a microbiome-based index. J Genet Genomics 2022; 49:685-688. [PMID: 35017120 DOI: 10.1016/j.jgg.2021.12.011] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 12/29/2022]
Affiliation(s)
- Zheng Sun
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Science, Qingdao 266101, China
| | - Xudong Liu
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Science, Qingdao 266101, China
| | - Gongchao Jing
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Science, Qingdao 266101, China
| | - Yuzhu Chen
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
| | - Shuaiming Jiang
- Department of Endocrinology, Hainan General Hospital, School of Food Science and Engineering, Hainan University, Haikou 570228, China
| | - Meng Zhang
- Key Laboratory of Dairy Biotechnology and Engineering, Inner Mongolia Agricultural University, Huhehot 010018, China
| | - Jiquan Liu
- Procter & Gamble Singapore Innovation Center, 138589, Singapore
| | - Jian Xu
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Science, Qingdao 266101, China
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China.
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11
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Cai Z, Lin S, Hu S, Zhao L. Structure and Function of Oral Microbial Community in Periodontitis Based on Integrated Data. Front Cell Infect Microbiol 2021; 11:663756. [PMID: 34222038 PMCID: PMC8248787 DOI: 10.3389/fcimb.2021.663756] [Citation(s) in RCA: 38] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2021] [Accepted: 05/31/2021] [Indexed: 02/05/2023] Open
Abstract
Objective Microorganisms play a key role in the initiation and progression of periodontal disease. Research studies have focused on seeking specific microorganisms for diagnosing and monitoring the outcome of periodontitis treatment. Large samples may help to discover novel potential biomarkers and capture the common characteristics among different periodontitis patients. This study examines how to screen and merge high-quality periodontitis-related sequence datasets from several similar projects to analyze and mine the potential information comprehensively. Methods In all, 943 subgingival samples from nine publications were included based on predetermined screening criteria. A uniform pipeline (QIIME2) was applied to clean the raw sequence datasets and merge them together. Microbial structure, biomarkers, and correlation network were explored between periodontitis and healthy individuals. The microbiota patterns at different periodontal pocket depths were described. Additionally, potential microbial functions and metabolic pathways were predicted using PICRUSt to assess the differences between health and periodontitis. Results The subgingival microbial communities and functions in subjects with periodontitis were significantly different from those in healthy subjects. Treponema, TG5, Desulfobulbus, Catonella, Bacteroides, Aggregatibacter, Peptostreptococcus, and Eikenella were periodontitis biomarkers, while Veillonella, Corynebacterium, Neisseria, Rothia, Paludibacter, Capnocytophaga, and Kingella were signature of healthy periodontium. With the variation of pocket depth from shallow to deep pocket, the proportion of Spirochaetes, Bacteroidetes, TM7, and Fusobacteria increased, whereas that of Proteobacteria and Actinobacteria decreased. Synergistic relationships were observed among different pathobionts and negative relationships were noted between periodontal pathobionts and healthy microbiota. Conclusion This study shows significant differences in the oral microbial community and potential metabolic pathways between the periodontitis and healthy groups. Our integrated analysis provides potential biomarkers and directions for in-depth research. Moreover, a new method for integrating similar sequence data is shown here that can be applied to other microbial-related areas.
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Affiliation(s)
- Zhengwen Cai
- State Key Laboratory of Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China.,National Clinical Research Center for Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China
| | - Shulan Lin
- State Key Laboratory of Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China.,National Clinical Research Center for Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China.,Department of Periodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
| | - Shoushan Hu
- State Key Laboratory of Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China.,National Clinical Research Center for Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China
| | - Lei Zhao
- State Key Laboratory of Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China.,National Clinical Research Center for Oral Diseases, West China College of Stomatology, Sichuan University, Chengdu, China.,Department of Periodontics, West China Hospital of Stomatology, Sichuan University, Chengdu, China
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Zhang Y, Jing G, Chen Y, Li J, Su X. Hierarchical Meta-Storms enables comprehensive and rapid comparison of microbiome functional profiles on a large scale using hierarchical dissimilarity metrics and parallel computing. BIOINFORMATICS ADVANCES 2021; 1:vbab003. [PMID: 36700101 PMCID: PMC9710644 DOI: 10.1093/bioadv/vbab003] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/20/2021] [Accepted: 05/06/2021] [Indexed: 01/28/2023]
Abstract
Functional beta-diversity analysis on numerous microbiomes interprets the linkages between metabolic functions and their meta-data. To evaluate the microbiome beta-diversity, widely used distance metrices only count overlapped gene families but omit their inherent relationships, resulting in erroneous distances due to the sparsity of high-dimensional function profiles. Here we propose Hierarchical Meta-Storms (HMS) to tackle such problem. HMS contains two core components: (i) a dissimilarity algorithm that comprehensively measures functional distances among microbiomes using multi-level metabolic hierarchy and (ii) a fast Principal Co-ordinates Analysis (PCoA) implementation that deduces the beta-diversity pattern optimized by parallel computing. Results showed HMS can detect the variations of microbial functions in upper-level metabolic pathways, however, always missed by other methods. In addition, HMS accomplished the pairwise distance matrix and PCoA for 20 000 microbiomes in 3.9 h on a single computing node, which was 23 times faster and 80% less RAM consumption compared to existing methods, enabling the in-depth data mining among microbiomes on a high resolution. HMS takes microbiome functional profiles as input, produces their pairwise distance matrix and PCoA coordinates. Availability and implementation It is coded in C/C++ with parallel computing and released in two alternative forms: a standalone software (https://github.com/qdu-bioinfo/hierarchical-meta-storms) and an equivalent R package (https://github.com/qdu-bioinfo/hrms). Supplementary information Supplementary data are available at Bioinformatics Advances online.
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Affiliation(s)
- Yufeng Zhang
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Gongchao Jing
- Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China
| | - Yuzhu Chen
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China
| | - Jinhua Li
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China,To whom correspondence should be addressed. or Jinhua Li
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao, Shandong 266071, China,Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, Shandong 266101, China,To whom correspondence should be addressed. or Jinhua Li
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